With increased reliance of all individuals, whether at work or at play, on access to various rich data sources, and with an increased demand to access such rich data sources on-the-road or on-the-run, comes a need for higher data bandwidth in portable computing and communication devices. For instance, users of mobile computing devices—from laptop computers, to cellular telephones, to wristwatches—desire access to more and more data, such as audio, streaming video, and other data-rich applications. There is thus a demand for systems and techniques that can deliver greater wireless bandwidth.
Part of the necessary process of providing high-bandwidth systems is the ability to “locate” an incoming signal and to align with the signal so that transmitted data may be extracted and read from the signal. This process becomes more complex with high bit rate communications. Also, higher bandwidth is sometimes achieved by separating a data stream into multiple simultaneous transmissions through various multiplexing processes. When such an approach is taken, the data in each transmitted signal must be identified and read, and all of the separate data must then be joined back together in the proper order.
Multiple-input and multiple-output (MIMO) communication methods attract considerable attention because of their potential to achieve much higher capacity than the traditional single-input single-output (SISO) methods. While MIMO systems based on space-time coding can be used to improve link reliability (e.g., by providing redundant information paths), MIMO systems incorporating spatial-multiplexing are used mainly to achieve high spectral efficiency. MIMO systems employing orthogonal frequency division multiplexing (OFDM) can achieve high spectral efficiency in rich scattering environments such as indoor wireless local area networks.
MIMO-OFDM systems can effectively exploit frequency diversity of frequency selective channels as well as spatial diversity of uncorrelated parallel wireless links. Bit interleaved coded modulation (BICM) used in conjunction with MIMO-OFDM and spatial multiplexing (SM) is particularly effective in exploring both spatial diversity and frequency selectivity without significant design efforts, and is well-suited for achieving very high data rates. See G. Caire et al., “Bit-interleaved coded modulation,” IEEE Trans. Inform. Theory, vol. 44, no. 3, pp. 927-946, May, 1998; A. M. Tonello, “Space-time bit-interleaved coded modulation with an iterative decoding strategy,” Proc. of IEEE Vehicular Technology Conference, pp. 473-478, Boston, September, 2000; B. Lu, X. Wang & K. R. Narayanan, “LDPC-based space-time coded OFDM systems over correlated fading channels: performance analysis and receiver design,” IEEE Trans. on Commun., vol. 50, no. 1, pp. 74-88, January, 2002; D. Park and B. G Lee, “Design criteria and performance of space-frequency bit-interleaved coded modulations in frequency selective Rayleigh fading channels,” Journal of Commun. and Networks, vol. 5, no. 2, pp. 141-149, June, 2003. In addition to exploiting spatial diversity, MIMO-OFDM systems retain the same advantages as SISO-OFDM systems, namely, reduced equalization complexity for frequency selective channels and an ability to explore frequency diversity through coding.
However, like their SISO-OFDM counterparts, MIMO-OFDM systems can be sensitive to synchronization errors, especially the carrier frequency offset (CFO). A good synchronization scheme is generally necessary to make a MIMO-OFDM system practically viable. Numerous techniques have been suggested in the literature for SISO-OFDM frequency synchronization. However, extension to MIMO systems tends to be difficult. The literature on MIMO-OFDM synchronization is relatively scarce. A training-based method has been proposed that relies on the cyclic prefix and the orthogonal polyphase sequences that can be modulated directly. See A. N. Mody & G. L. Stuber, “Synchronization for MIMO OFDM systems,” in Globecom 2001, San Antonio, Tex., November, 2001. Although simple, the use of training sequences for CFO recovery significantly reduces achievable data rate. A blind technique proposed for single-input multiple-output (SIMO) antenna systems can be extended to MIMO systems. See T. Jiang & N. D. Sidiropoulos, “A direct blind receiver for SIMO and MIMO OFDM systems subject to unknown frequency offset and multipath,” Submitted to SPAWC 2003, Rome, Italy, June, 2003. But this approach requires too long a delay due to iteration over a large number of OFDM symbols. Therefore, it is not a good solution for delay-sensitive applications.
Accurate channel estimation is also important in realizing the full performance potential of MIMO-OFDM systems. Channel estimation becomes a major challenge as the number of channel responses that need be characterized increases substantially, as the number of transmit and receive antennas increases. Previous works exist that identify desirable training patterns for estimating channel responses for MIMO systems. See, e.g., Y. (G.) Li, N. Seshadri, and S. Ariyavisitakul, “Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels,” IEEE J. Select Areas Commun., vol. 17, pp. 461-471, March, 1999; Y. (G.) Li, L. J. Cimini, and N. R. Sollegberger, “Robust channels estimation for OFDM systems with rapid dispersive fading channels,” IEEE Trans. Commun., vol. 46, pp. 902-915, July, 1998; Y. (G.) Li, “Simplified channel estimation for OFDM systems with multiple transmit antennas,” IEEE Trans. Wireless Commun., vol. 1, pp. 67-75, January, 2002; X. Ma et al., “Optimal training for MIMO frequency-selective fading channels,” IEEE Trans. Wireless Commun., January, 2004, accepted for publication. Other works also appear in this area. See, e.g., H. V Poor “An introduction to signal detection and estimation,” Spring-Verlag: New York, 1994.
There remains a need for systems and techniques that can provide extremely high wireless bandwidth in a reliable and cost-effective manner.